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• W2593767219 abstract "The identification of good predictive biomarkers allows investigators to optimize the target population for a new treatment. We propose a novel utility-based Bayesian population finding (BaPoFi) method to analyze data from a randomized clinical trial with the aim of finding a sensitive patient population. Our approach is based on casting the population finding process as a formal decision problem together with a flexible probability model, Bayesian additive regression trees (BART), to summarize observed data. The proposed method evaluates enhanced treatment effects in patient subpopulations based on counter-factual modeling of responses to new treatment and control for each patient. In extensive simulation studies, we examine the operating characteristics of the proposed method. We compare with a Bayesian regression-based method that implements shrinkage estimates of subgroup-specific treatment effects. For illustration, we apply the proposed method to data from a randomized clinical trial." @default.
• W2593767219 created "2017-03-16" @default.
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• W2593767219 date "2017-03-03" @default.
• W2593767219 modified "2023-10-18" @default.
• W2593767219 title "Bayesian population finding with biomarkers in a randomized clinical trial" @default.
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• W2593767219 doi "https://doi.org/10.1111/biom.12677" @default.
• W2593767219 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5582025" @default.
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